Practice Mid-Term Exam

Slides:



Advertisements
Similar presentations
Lesson 10: Linear Regression and Correlation
Advertisements

Regression Inferential Methods
Descriptive Statistics: Numerical Measures
Chap 3-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 3 Describing Data: Numerical Statistics for Business and Economics.
Correlation & Regression Math 137 Fresno State Burger.
Correlation and Linear Regression
Correlation and Linear Regression
McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 13 Linear Regression and Correlation.
Linear Regression and Correlation
Describing distributions with numbers
Chapter 3 - Part B Descriptive Statistics: Numerical Methods
1 1 Slide © 2014 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.
Correlation and Regression
Data Analysis 17 Data are obtained from a random sample of adult women with regard to their ages and their monthly expenditures on health products. The.
1 1 Slide © 2009 Thomson South-Western. All Rights Reserved Slides by JOHN LOUCKS St. Edward’s University.
6.1 What is Statistics? Definition: Statistics – science of collecting, analyzing, and interpreting data in such a way that the conclusions can be objectively.
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 3 Descriptive Statistics: Numerical Methods.
© 2008 Brooks/Cole, a division of Thomson Learning, Inc. 1 Chapter 4 Numerical Methods for Describing Data.
Regression. Height Weight How much would an adult female weigh if she were 5 feet tall? She could weigh varying amounts – in other words, there is a distribution.
Regression. Height Weight Suppose you took many samples of the same size from this population & calculated the LSRL for each. Using the slope from each.
Chapter 3 Descriptive Statistics: Numerical Methods Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.
Production Planning and Control. A correlation is a relationship between two variables. The data can be represented by the ordered pairs (x, y) where.
1 1 Slide Slides Prepared by JOHN S. LOUCKS St. Edward’s University © 2002 South-Western/Thomson Learning.
Describing distributions with numbers
McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 3 Descriptive Statistics: Numerical Methods.
Introduction to Probability and Statistics Thirteenth Edition Chapter 12 Linear Regression and Correlation.
Variation This presentation should be read by students at home to be able to solve problems.
Regression with Inference Notes: Page 231. Height Weight Suppose you took many samples of the same size from this population & calculated the LSRL for.
Chapter 3, Part B Descriptive Statistics: Numerical Measures n Measures of Distribution Shape, Relative Location, and Detecting Outliers n Exploratory.
LECTURE 9 Tuesday, 24 FEBRUARY STA291 Fall Administrative 4.2 Measures of Variation (Empirical Rule) 4.4 Measures of Linear Relationship Suggested.
1 Chapter 4 Numerical Methods for Describing Data.
Essential Statistics Chapter 51 Least Squares Regression Line u Regression line equation: y = a + bx ^ –x is the value of the explanatory variable –“y-hat”
Linear Regression and Correlation Chapter GOALS 1. Understand and interpret the terms dependent and independent variable. 2. Calculate and interpret.
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics Seventh Edition By Brase and Brase Prepared by: Lynn Smith.
Larson/Farber Ch 2 1 Elementary Statistics Larson Farber 2 Descriptive Statistics.
©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Linear Regression and Correlation Chapter 13.
AP Statistics Section 15 A. The Regression Model When a scatterplot shows a linear relationship between a quantitative explanatory variable x and a quantitative.
(Unit 6) Formulas and Definitions:. Association. A connection between data values.
Chapter 13 Linear Regression and Correlation. Our Objectives  Draw a scatter diagram.  Understand and interpret the terms dependent and independent.
Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc.. Chap 7-1 Day 2 Lecture Review of Descriptive Statistics.
Quantitative Methods in the Behavioral Sciences PSY 302
Correlation and Linear Regression
Thursday, May 12, 2016 Report at 11:30 to Prairieview
Regression and Correlation
Bellwork 1. Order the test scores from least to greatest: 89, 93, 79, 87, 91, 88, Find the median of the test scores. 79, 87, 88, 89, 91, 92, 93.
Correlation & Regression
Math 21 Midterm Review Part 1: Chapters 1-4.
Regression.
LECTURE 13 Thursday, 8th October
Chapter 5 STATISTICS (PART 4).
Regression.
Unit 4 EOCT Review.
Correlation and Regression
Common Core Math I Unit 6 One-Variable Statistics Introduction
Common Core Math I Unit 6 One-Variable Statistics Introduction
Chapter 12 Regression.
Regression.
Regression.
BUS173: Applied Statistics
Common Core Math I Unit 6 One-Variable Statistics Introduction
Regression.
Regression Chapter 8.
Regression.
Correlation and Regression
CHAPTER 12 More About Regression
Regression.
Correlation & Regression
Honors Statistics Review Chapters 7 & 8
Linear Regression and Correlation
Presentation transcript:

Practice Mid-Term Exam Question 1. Classify the following random variable as to whether it is discrete or continuous. The number of people served at a McDonalds on a certain day. Discrete Continuous

Question 2. A frequency distribution based on six classes gave the following relative frequencies for the first five classes: 0.06, 0.10, 0.18, 0.26, 0.24. What is the relative frequency for the remaining class? 0.05 0.10 0.16 0.20

Question 3. In order to rate TV shows, phone surveys are sometimes used. Such a survey might record several variables. Which of the following variables is qualitative? The number of persons watching the show. The ages of all persons watching the show. The number of times the show has been watched in the last month. The name of the show being watched.

Question 4. When the sample mean is larger than the sample median, the distribution of the sample is Approximately symmetric Skewed right Skewed left Heavy tailed

Question 5. Consider the following two samples: Which sample has the largest standard deviation? Sample 1 Sample 2 The standard deviations will be equal Can’t tell from the data

Question 6. The time required to assemble a bicycle has a mean of 42 minutes and a standard deviation of 5 minutes. The distribution of assembly times is mound shaped. What percent of assembly times are between 37 and 47 minutes? approximately 68% approximately 81.5% approximately 89% approximately 95%

Question 7. The scores of the top ten finishers in a women’s golf tournament are listed below. 71 67 67 72 76 72 73 68 72 72 Find the median score. 67 72 71 74

Question 8. Given the following five number summary, find the interquartile range (IQR). 29 37 50 66 94 29 50 65 32.5

Question 9. Given the following least squares prediction equation. = – 173 + 74x, we estimate y to ________ by _________ with each 1-unit increase in x. increase, 74 decrease, 74 decrease, 173 increase, 173

Question 10. In statistics, what is a sample? A set of data that characterizes some phenomenon. A set of data selected from a population. The number of measurements on a particular subset of the population. A number that represents an estimate for a population parameter. An inference about a population.

Question 11. If all the measurements in a large data set are approximately the same magnitude except for a few measurements that are very much smaller than the other measurements, how would the mean and median of the data set compare to each other and what shape would the histogram of the data set have? (a) The mean would be larger than the median and the histogram would be skewed to the left. (b) The mean would be smaller than the median and the histogram would be skewed to the left. (c) The mean would be equal to the median and the histogram would be symmetrical. (d) The mean would be smaller than the median and the histogram would be skewed to the right. (e) The mean would be larger than the median and the histogram would be skewed to the right.

Question 12. The correlation between two variables, x and y, is .956. State the coefficient of determination and explain just what this coefficient "determines".

Question 13. In a bell‑shaped distribution, what is the approximate percent of the measurements have a z‑score between ‑2 and 2?

Question 14. The boxplots below are of female and male exercise per week in hours. Compare and contrast the results of the two samples. Be sure to tell all that the boxplots reveal about the two groups of students. ‑‑‑‑‑‑‑‑ Female ‑‑‑I + I‑‑‑‑ ** * O O ‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑ Male ‑‑‑‑‑I + I‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑‑ ‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑‑+‑‑‑‑‑‑‑‑ 4.0 8.0 12.0 16.0 20.0

Question 15. The least-squares regression line of y on x is The line which makes the coefficient of determination as small as possible. The line that minimizes the square of the vertical distance between observed values of y and those predicted by the line, ŷ. The line which best splits the data in half, with half of the points above the line and half below the line. The line which best predicts the value of x for a given value of y. The line that connects all the points None of the above.

Question 16. In which scatter diagram is r = 0.9811? b) c) d) e)

Question 17. For a scatter diagram of (x, y) pairs, the coefficient of determination is 80%. This means: 80% of the data points are in the confidence interval. We are 80% confident that the least squares-regression line is correct. The correlation coefficient is 0.64. There is a strong positive correlation between x and y. 80% of the total variation in the response variable is explained by the least squares regression line.

Question 18. A faculty group wants to determine whether job rating (x) is a useful linear predictor of raise (y). Using the method of least squares, the faculty group obtained the following prediction equation: ŷ = $14,000 – $2,000x. The estimated slope of the line is for a 1-point increase in an administrator’s rating, we estimate the administrator’s raise to decrease $2,000. TRUE FALSE

Question 19. The number of absences (x) and the final grade (y) of 9 randomly selected statistics students yielded the following least-squares regression equation: ŷ = -2.75x + 96.14 Determine the predicted final grade of a student who had 6 absences. Determine the residual of a data point for which x = 8 and y = 76.

Question 20. An engineer wants to use the weight of a car to predict the gas mileage. The following fitted line plot gives the least-squares regression line based on the weight (in hundreds of pounds) and miles per gallon for 15 domestic cars.

Which of the following could be the equation of the least-squares regression line depicted by the fitted line plot? (need hats on Miles per gallon) Miles per gallon = 44.3 - 0.7*weight (in hundreds of pounds) Miles per gallon = 44.3 + 0.7* weight (in hundreds of pounds) Miles per gallon = 27.1 - 1.2* weight (in hundreds of pounds) Miles per gallon = 27.1 + 1.2* weight (in hundreds of pounds) None of a), b), c), or d) is correct.

What is the predicted miles per gallon for a car weighing 3100 pounds? Less than 14.5 miles per gallon. At least 14.5 miles per gallon, but less than 16.5 miles per gallon. At least 16.5 miles per gallon, but less than 18.5 miles per gallon. At least 18.5 miles per gallon, but less than 20.5 miles per gallon. At least 20.5 miles per gallon.